[Fix] update docker image for InternNav v0.3.0 (#8)

* fix docker image 030

* add config note for agent server

Author: kew6688

source/en/user_guide/internnav/quick_start/evaluation.md

@@ -11,10 +11,25 @@ Model weights of InternVLA-N1 (Dual System) can be downloaded from [InternVLA-N1
 ### Evaluation on Isaac Sim
 Before evaluation, we should download the robot assets from [InternUTopiaAssets](https://huggingface.co/datasets/InternRobotics/Embodiments) and move them to the `data/` directory.
 
-[UPDATE] We support using local model and isaac sim in one process now. Evaluate on Single-GPU:
+InternNav supports two execution modes for running the model during evaluation. 
+
+#### 1) In-Process Mode (use_agent_server = False)
+We now support running the local model and Isaac Sim in the same process, enabling single-GPU evaluation without launching a separate agent service, and also supports multi-process execution, where each process hosts its own simulator and local model.
 
 ```bash
 python scripts/eval/eval.py --config scripts/eval/configs/h1_internvla_n1_async_cfg.py    
+
+# set config with the following fields
+eval_cfg = EvalCfg(
+    task=TaskCfg(
+        task_settings={
+            'use_distributed': False,       # disable Ray-based distributed evaluation 
+        }
+    ),
+    eval_settings={
+        'use_agent_server': False,          # run the model in the same process as the simulator
+    },
+)
 ```
 
 For multi-gpu inference, currently we support inference on environments that expose a torchrun-compatible runtime model (e.g., Torchrun or Aliyun DLC).
@@ -30,8 +45,8 @@ For multi-gpu inference, currently we support inference on environments that exp
     --config scripts/eval/configs/h1_internvla_n1_async_cfg.py
 ```
 
-The main architecture of the whole-system evaluation adopts a client-server model. In the client, we specify the corresponding configuration (*.cfg), which includes settings such as the scenarios to be evaluated, robots, models, and parallelization parameters. The client sends requests to the server, which then submits tasks to the Ray distributed framework based on the corresponding cfg file, enabling the entire evaluation process to run.
-
+#### 2) Agent Server Mode (use_agent_server = True)
+We also support running the model in a separate process. 
 First, change the 'model_path' in the cfg file to the path of the InternVLA-N1 weights. Start the evaluation server:
 ```bash
 # from one process
@@ -44,6 +59,13 @@ Then, start the client to run evaluation:
 # from another process
 conda activate <internutopia>
 MESA_GL_VERSION_OVERRIDE=4.6 python scripts/eval/eval.py --config scripts/eval/configs/h1_internvla_n1_async_cfg.py
+
+# set config with the following fields
+eval_cfg = EvalCfg(
+    eval_settings={
+        'use_agent_server': True,          # run the model in the same process as the simulator
+    },
+)
 ```
 
 The evaluation results will be saved in the `eval_results.log` file in the output_dir of the config file. The whole evaluation process takes about 10 hours at RTX-4090 graphics platform.

source/en/user_guide/internnav/quick_start/simulation.md

@@ -14,7 +14,7 @@ To help you get started quickly, we've prepared a **Docker image** pre-configure
 
 You can pull the image (~17GB) and run evaluations in the container using the following command:
 ```bash
-docker pull crpi-mdum1jboc8276vb5.cn-beijing.personal.cr.aliyuncs.com/internrobotics/internnav:v0.2.0
+docker pull crpi-mdum1jboc8276vb5.cn-beijing.personal.cr.aliyuncs.com/internrobotics/internnav:v0.3.0
 ```
 
 Run the container by:
@@ -40,12 +40,11 @@ docker run --name internnav -it --rm --gpus all --network host \
   -v ${HOME}/docker/isaac-sim/data:/root/.local/share/ov/data:rw \
   -v ${HOME}/docker/isaac-sim/documents:/root/Documents:rw \
   -v ${PWD}/data/scene_data/mp3d_pe:/isaac-sim/Matterport3D/data/v1/scans:rw \
-  crpi-mdum1jboc8276vb5.cn-beijing.personal.cr.aliyuncs.com/internrobotics/internnav:v0.2.0
+  crpi-mdum1jboc8276vb5.cn-beijing.personal.cr.aliyuncs.com/internrobotics/internnav:v0.3.0
 ```
-After the container started, you can quickly start the env and install the InternNav:
+After the container started, you can quickly start to use the InternNav with conda environment:
 ```bash
 conda activate internutopia
-pip install -e .[isaac,model]
 ```
 <!-- To help you get started quickly, we've prepared a Docker image pre-configured with Isaac Sim 4.5 and InternUtopia. You can pull the image and run evaluations in the container using the following command:
 ```bash